The present invention relates generally to systems for detecting, counting and measuring the amplitude of step-like signal events such as those produced by preamplifiers attached to radiation detectors used to detect x-rays, gamma-rays, nuclear particles, and the like. More particularly, it relates to increasing the accuracy with which the amplitudes of these step-like events are measured by applying time variant filtering, indexing determined amplitudes according to the parameters of the applied filter, and using the index values to sort the measured amplitudes into a set of spectra on an event-by-event basis. By appropriately processing signals between the events, baseline corrections can also be employed.
The specific embodiments described relate to processing step-like signals generated by detector systems in response to absorbed radiation or particles and, more particularly, to digitally processing such step-like signals in high resolution, high rate x-ray spectrometers with reset feedback preamplifiers. The method can also be applied to gas proportional counters, scintillation detectors, and gamma-ray (γ-ray) spectrometers with resistive feedback preamplifiers, and, more generally, to any signals that have the described characteristics. The application associated with the described specific embodiment, namely the detection of dilute elements in ore bodies, is given particular attention only because this was the area in which the method was first developed.
The techniques that we have developed should therefore should not be construed as being limited to this specific application. Any detection system, for example, that produces output current pulses that are integrated by charge sensitive preamplifiers could be treated by these techniques, whether the detected quantities are light pulses, x-rays, nuclear particles, chemical reactions, or otherwise. Other detection systems that produce output signals that are electrically equivalent can be similarly processed: for example the current output of a photomultiplier tube attached to a scintillator produces a signal of this type.
A Synopsis of Current Spectrometer Art
FIG. 1 shows a schematic diagram of a prior art radiation spectroscopy system employed with a solid state detector diode 7. Similar systems are used for measuring x-ray, gamma-ray and alpha and beta particle radiations, differing primarily in the physical form of the detector diode 7, which might also be replaced with a proportional counter or other detector. All of these detectors 7 share the common property that, when biased by a voltage supply 8, they produce an output current pulse when detecting an absorption event and the total charge QE in this pulse is approximately proportional to the energy E of the absorbed ray or particle. This current flows into a preamplifier 10, where it is integrated onto feedback capacitor 13 by amplifier 12, whose output is then a step of amplitude Ae=QE/Cf, where Cf is the capacitance of feedback capacitor 13.
A spectroscopy amplifier 15 is then used to measure AE. Within modern spectroscopy amplifiers 15 the output of preamplifier 10 is typically sent to both a “slow” energy filter circuit 17 and a “fast” pileup inspection circuit 18. The energy filter circuit filters the Ae step to produce a low noise, shaped output pulse whose peak height is proportional to AE. The pileup inspection circuit applies a fast filter and discriminator to the preamplifier output to detect Ae signal steps (events) and signals the filter peak capture circuit 20 to capture amplitudes from those shaped pulses produced by the energy filter circuit 17 which have sufficient time separation so that they do not distort each other's amplitudes (i.e., do not “pile up”). The distinction between the “fast and “slow” filters is relative, based on the particular application, but the “fast” filter's time constant is typically at least 10 times shorter than the “slow” energy filter's (e.g., a 200 ns fast filter and a 4 μs energy filter in a typical x-ray spectrometer). The inspection circuit 18 also determines when events are sufficiently separated so that the output of the energy filter circuit has returned to its DC (i.e., non-zero offset) value and signals the baseline capture circuit 22 to capture these values so that they may be subtracted from captured peak values by the subtraction circuit 23. These differences are then passed to a multichannel analyzer (MCA) or digital signal processor (DSP) 25, for binning to form a spectral representation (spectrum) of the energy values present in the incident radiation.
The art of building spectroscopy amplifiers is relatively mature and many variations, using both analog and digital electronics, exist on the basic circuit shown in FIG. 1. The reference book by Knoll provides a good introduction to the subject [KNOLL—1989]. Further discussion may be found in U.S. Pat. Nos. 5,684,850, 5,870,051, 5,873,054 and 6,609,075B1 by Warburton et al. [WARBURTON—1997, 1999A, 1999B, and 2003]. For example, in some designs the filter peak capture circuit senses and captures peaks autonomously and the job of the pileup inspector is to discriminate between valid and invalid capture and only allow valid values to pass on to the subtraction circuit 23. Moreover, the order of the shown components may be altered to achieve the same ends. Thus, in analog circuits the baseline capture circuit is commonly a switched capacitor which is tied to the output of the energy filter circuit 17 as long as the baseline is valid and disconnected whenever the pileup inspection circuit detects a event. The time constant of this circuit is long enough to filter baseline noise. The subtraction circuit 23 is then typically an operational amplifier with the capacitor voltage applied to its negative input and the peak capture circuit 20 applied to its positive input. In some cases, even the order of circuits 20 and 23 is reversed, so the offset is removed from the energy filter circuit's output before peak amplitudes are captured. In traditional MCA's, in fact, the peak capture capability is included in the MCA 25 and removed entirely from spectroscopy amplifier 15. Digital spectrometers are slightly different in that they capture only single baseline values, which have the same noise as the signal. In this case the baseline values are typically averaged to reduce their noise and the average <b> is then presented to subtraction circuit 23. The net result is the same, however, and the basic functions presented in FIG. 1 capture the essence of the operation of these spectrometers as a class.
Pileup Inspection and the Count Rate/Energy Resolution Tradeoff
As noted above, event amplitudes are found by filtering (or “shaping”) the preamplifier signal. In the simplest case, a digital trapezoidal filter, this just means forming averages of the preamplifier signal after and before the event and taking the difference of the two. The error in the measurement is then the sum of the errors in the two averages, added in quadrature. To the extent that the noise has a white power spectrum (i.e., series noise) the errors in the averages can be reduced by increasing the averaging time. This is the common regime for high count rate operation, where extending the averaging time (i.e., peaking time) improves energy resolution. If, however, another event arrives during the averaging time, then the measurement is spoiled (for both events) and they are said to have “piled up” because the output signal from the energy filter is the sum of the shaped pulses from the two events piled on top of each other. While other digital or analog shaping amplifiers may employ more complex filters, the same basic constraint applies: using a longer peaking (shaping) time improves energy resolution but increases pileup, while using a shorter peaking time increases throughput but degrades energy resolution. Hence, with state-of-the-art spectrometers, selecting a peaking time at which to operate is therefore always a compromise that allows an adequate number of counts to be collected into the spectrum at an acceptable resolution.
The number of counts that do not pile up is readily determined, assuming paralyzing dead times and random event arrival times, from Poisson statistics, which gives the familiar throughput formula relating output counting rate (OCR) to input counting rate (ICR):OCR=ICR exp(−ICR*τd).  (1)Here the deadtime τd is related to the signal averaging time and, in good spectrometers is approximately the base width of the shaped pulse. In a modern digital spectrometer with trapezoidal filtering, the deadtime is 2*(peaking time τp+flattop time τg) [WARBURTON—2003]. The maximum in Eqn. 1 is OCRmax=exp(−1)/τd, at ICRmax=1/τd, showing that the τp (and hence τd) that optimizes throughput depends upon ICR.
While, from Eqn. 1, reducing τd always increases throughput, the required τp reduction also degrades energy resolution, which may be unacceptable in a real life application. For example, FIG. 2A shows a weak spectral line sitting on a significant background counting rate. Many important detection problems are of this class, including quantifying dilute element concentrations in complex substances, detecting small amounts of radioactive material against a natural background, or measuring a weak fluorescence process in the presence of stronger elastic scattering. Typically the counts in peak P2 is found by summing the counts in the P2 region and then subtracting a background estimated from measurements in the regions B1 and B2. Because peaks seldom occur in isolation, realistic situations more commonly resemble the figures FIG. 2B or FIG. 2C, where the region for making the background measurement becomes constrained by the difference between the inter-peak separations and the spectrometer's resolution. Clearly, for example, if the energy resolution were improved by a factor of 2 in FIG. 2B and FIG. 2C, then, relatively speaking, FIG. 2B would resemble FIG. 2A and FIG. 2C would resemble FIG. 2B in terms of the relative number of channels available to make peak and background measurements. Indeed, from the point of view of detection limit, the smaller the value of the spectrometer's energy resolution ΔE, the better, since the number of counts from the spectral line S2 contributing to P2 will remain constant as ΔE decreases, but the number of background counts will decrease in proportion to ΔE. Hence both the fractional error due to background decreases and the accuracy with which it is known increases as the regions B1 and B2 increase. From this point of view, therefore, the peaking time τp should be made as long as possible in order to decrease ΔE. This leads to a fundamental dilemma, since beyond a certain point the loss of counts due to pileup will negate any further gains due to improved resolution.
Time Variant Filtering Methods
Time variant filtering attempts to optimize this tradeoff by adjusting the signal averaging time on an event-by-event basis. FIG. 3 shows the situation, with a preamplifier trace 40 having 7 events. Filtered using a 1 μs trapezoidal filter 42, five of the events are filtered correctly and two (numbers 5 & 6) pileup. Filtered with a 2 μs trapezoidal filter 43, only two of the events are filtered correctly (numbers 3 & 4) and the rest pile up. However, in the time variant approach, first devised by Koeman and later developed and commercialized by others [KOEMAN—1975, LAKATOS—1990, AUDET—1994, and MOTT—1994], the entire interval between each pair of events is used for the signal averaging process. In some cases a simple running sum average is employed, but more typically the full set of points is recorded into the memory of a digital signal processor and filter function weights are applied, the particular set of weights being selected depending upon the length of the interval. These intervals are represented in FIG. 3 as horizontal lines 47 that exclude the region of the event risetimes. We notice that, if simple running averages were used, as an extension of the symmetrical trapezoidal filtering case, each event is processed by an asymmetrical trapezoid whose risetime is the time to the preceding event and whose falltime is the time to the following event as suggested by the dotted traces 48 below curve.
Time variant filtering has recognized advantages and disadvantages. The main advantage is that it is efficient: all events separated by some minimum allowed measurement time are processed and as much information is used as is available. Hence, for a given counting rate, the time variant processor not only achieves a higher throughput but also a better energy resolution. The method has, however, three important disadvantages. First, its spectral response function is non-Gaussian, being built up of many Gaussians with different resolutions corresponding to the range of variable shaping times used in processing the events. This, per se, is not bad. The second problem is that the shape of the response function varies with ICR, since the number of events being processed with longer and shorter processing times varies with rate. This fact disqualifies the method for use in the majority of analysis methods, which are based on the use of standard material measurements. Because there is no way to guarantee that the standards and unknowns can be collected at identical ICRs, the peak shapes cannot be accurately compared between the two and the analysis methods fail. Finally, because these time variant filters use all the available data to process the events, there is no data left over for making baseline measurements, which degrades resolution and makes it unpredictable as well. It would therefore be desirable, particularly in situations where the goal is to measure weak lines against large backgrounds, to have an event processing method that improved throughput and resolution without the penalty of having a poorly defined or undefined response function. It would be further useful to be able to make baseline measurements to stabilize the response function at high counting rates and against other sorts of variations.